Book Image

Mastering NLP from Foundations to LLMs

By : Lior Gazit, Meysam Ghaffari
Book Image

Mastering NLP from Foundations to LLMs

By: Lior Gazit, Meysam Ghaffari

Overview of this book

Do you want to master Natural Language Processing (NLP) but don’t know where to begin? This book will give you the right head start. Written by leaders in machine learning and NLP, Mastering NLP from Foundations to LLMs provides an in-depth introduction to techniques. Starting with the mathematical foundations of machine learning (ML), you’ll gradually progress to advanced NLP applications such as large language models (LLMs) and AI applications. You’ll get to grips with linear algebra, optimization, probability, and statistics, which are essential for understanding and implementing machine learning and NLP algorithms. You’ll also explore general machine learning techniques and find out how they relate to NLP. Next, you’ll learn how to preprocess text data, explore methods for cleaning and preparing text for analysis, and understand how to do text classification. You’ll get all of this and more along with complete Python code samples. By the end of the book, the advanced topics of LLMs’ theory, design, and applications will be discussed along with the future trends in NLP, which will feature expert opinions. You’ll also get to strengthen your practical skills by working on sample real-world NLP business problems and solutions.
Table of Contents (14 chapters)

Key technical trends around LLMs and AI

In this section, we cover what we identify as key trends in the field of NLP and LLMs.

We will start with the technical trends, and later, we will touch on the softer cultural trends.

Computation power – the engine behind LLMs

As technology has advanced, especially in computing, many areas in tech have thrived, particularly NLP and LLMs. It’s not just about faster calculations and bigger parameter space; it’s about new possibilities and reshaping our digital world. In this section, we’ll explore how this growth in computing has been foundational for NLP and LLMs today, focusing on their purpose, worth, and influence.

Purpose – paving the way for progress

In the initial days of AI and ML, the models were rudimentary—not due to a lack of imagination or intent, but because of restrictive computational boundaries. Tasks that we now consider basic, such as simple pattern recognitions, were...